docstr: Tests transformer head loss when truth is empty and non-empty.
docstr_tok: ["Tests","transformer","head","loss","when","truth","is","empty","and","non-empty","."]
code: def test_detr_head_loss(self):
      """Tests transformer head loss when truth is empty and non-empty."""
      s = 256
      metainfo = {
          "img_shape": (s, s),
          "scale_factor": (1, 1),
          "pad_shape": (s, s),
          "batch_input_shape": (s, s),
      }
      img_metas = DetDataSample()
      img_metas.set_metainfo(metainfo)
      batch_data_samples = []
      batch_data_samples.append(img_metas)
  
      config = get_detector_cfg("detr/detr_r50_8xb2-150e_coco.py")
  
      model = MODELS.build(config)
      model.init_weights()
      random_image = torch.rand(1, 3, s, s)
  
      # Test that empty ground truth encourages the network to
      # predict background
      gt_instances = InstanceData()
      gt_instances.bboxes = torch.empty((0, 4))
      gt_instances.labels = torch.LongTensor([])
      img_metas.gt_instances = gt_instances
      batch_data_samples1 = []
      batch_data_samples1.append(img_metas)
      empty_gt_losses = model.loss(
          random_image, batch_data_samples=batch_data_samples1
      )
      # When there is no truth, the cls loss should be nonzero but there
      # should be no box loss.
      for key, loss in empty_gt_losses.items():
          if "cls" in key:
              self.assertGreater(
                  loss.item(), 0, "cls loss should be non-zero"
              )
          elif "bbox" in key:
              self.assertEqual(
                  loss.item(),
                  0,
                  "there should be no box loss when no ground true boxes",
              )
          elif "iou" in key:
              self.assertEqual(
                  loss.item(),
                  0,
                  "there should be no iou loss when there are no true boxes",
              )
  
      # When truth is non-empty then both cls and box loss should be nonzero
      # for random inputs
      gt_instances = InstanceData()
      gt_instances.bboxes = torch.Tensor(
          [[23.6667, 23.8757, 238.6326, 151.8874]]
      )
      gt_instances.labels = torch.LongTensor([2])
      img_metas.gt_instances = gt_instances
      batch_data_samples2 = []
      batch_data_samples2.append(img_metas)
      one_gt_losses = model.loss(
          random_image, batch_data_samples=batch_data_samples2
      )
      for loss in one_gt_losses.values():
          self.assertGreater(
              loss.item(),
              0,
              "cls loss, or box loss, or iou loss should be non-zero",
          )
  
      model.eval()
      # test _forward
      model._forward(
          random_image, batch_data_samples=batch_data_samples2
      )
      # test only predict
      model.predict(
          random_image,
          batch_data_samples=batch_data_samples2,
          rescale=True,
      )
  
file_path: mmdetection/tests/test_models/test_detectors/test_detr.py
imports: from unittest import TestCase
  import torch
  from mmengine.structures import InstanceData
  from mmdet.registry import MODELS
  from mmdet.structures import DetDataSample
  from mmdet.testing import get_detector_cfg
  from mmdet.utils import register_all_modules
label_desc: Use image node_id 2 for calling the TestDETR obj's underlying member method code with example usage: obj.test_detr_head_loss() without return types